@inproceedings {740,
	title = {First principles study on the use of pristine Ti$_{3}$C$_{2}$ MXene as a carrier for anticancer drug 5-Fluorouracil},
	booktitle = {Proceedings of the 44th Samahang Pisika ng Pilipinas Physics Conference},
	year = {2026},
	month = {17-20 Jun 2026},
	pages = {SPP-2026-3B-04},
	address = {Los Ba{\~n}os},
	abstract = {Cancer remains one of the most life-threatening diseases in the Philippines. However, current cancer treatments may be limited in their efficacy and cause high therapeutic risks. Thus, to ensure medication is delivered in a way that allows the drug to reach its desired location without causing harm to surrounding tissues, innovations in drug delivery systems are a developing field of interest. In this study, we investigated the application of pristine Ti$_{3}$C$_{2}$ MXenes as a carrier for the anticancer drugs 5-Fluorouracil (5-FU) using density functional theory. We found that the adsorption of the molecule was thermodynamically favorable for two out of three of the tested binding sites. These results aim to contribute to the developing field of nanomaterials in drug delivery systems.},
	url = {https://proceedings.spp-online.org/article/view/SPP-2026-3B-04},
	author = {Adrianna Victoria Beatrice J. Pantoja and Haley Osment S. Napalcruz and Gennevieve M. Macam}
}
@inproceedings {681,
	title = {Prediction of orthorhombic lattice constants using machine learning},
	booktitle = {Proceedings of the 42nd Samahang Pisika ng Pilipinas Physics Conference},
	year = {2024},
	month = {3{\textendash}6 Jul 2024},
	pages = {SPP-2024-PB-22},
	address = {Batangas City},
	abstract = {A crystal structure is composed of a unit cell repeating itself to occupy a space, forming what is known as a lattice. This arrangement is dictated by the structure{\textquoteright}s lattice constants. Lattice constants are integral for investigation into the properties of crystal materials. However, current methods to determine such constants may be computationally exhaustive and time consuming. In this study, we utilized a random forest machine learning model to predict the lattice constants of orthorhombic crystal structures. This model was trained using the various materials{\textquoteright} structural properties. To quantitatively evaluate the quality of our our model, we compared the model generated lattice constants with the experimental values, and obtained the following coefficients of determination (R{\texttwosuperior}): 0.860, 0.825, and 0.826 for the a, b, and c constants respectively, which we found to be similar with previous studies. Moreover, we found the resultant mean squared error and mean absolute error for each lattice constant to be minimal, further supporting the overall performance of our model. Furthermore, to illustrate the weight of each property on the training of the model, we calculated the feature importance across the three random forest regressors. We found the key features to be unit cell volume, crystal system type, mean atomic number, and total atomic number.},
	url = {https://proceedings.spp-online.org/article/view/SPP-2024-PB-22},
	author = {David D. Daffon and Adrianna Victoria Beatrice J. Pantoja and Gennevieve M. Macam}
}